


from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       logging.get_logger(__name__)

# General docstring
_SCREAMING_SNAKE_CASE     :					List[Any]      =       "RegNetConfig"

# Base docstring
_SCREAMING_SNAKE_CASE     :					Any      =       "facebook/regnet-y-040"
_SCREAMING_SNAKE_CASE     :					str      =       [1, 10_88, 7, 7]

# Image classification docstring
_SCREAMING_SNAKE_CASE     :					Tuple      =       "facebook/regnet-y-040"
_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       "tabby, tabby cat"

_SCREAMING_SNAKE_CASE     :					Tuple      =       [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case = 3     ,   __snake_case = 1     ,   __snake_case = 1     ,   __snake_case = "relu"     ,   ):
         super().__init__()
         snake_case											=							nn.Convad(
             __snake_case     ,   __snake_case     ,   kernel_size=__snake_case     ,   stride=__snake_case     ,   padding=kernel_size // 2     ,   groups=__snake_case     ,   bias=__snake_case     ,   )
         snake_case											=							nn.BatchNormad(__snake_case       )
         snake_case											=							ACTaFN[activation] if activation is not None else nn.Identity()




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.convolution(__snake_case       )
         snake_case											=							self.normalization(__snake_case       )
         snake_case											=							self.activation(__snake_case       )
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         super().__init__()
         snake_case											=							RegNetConvLayer(
             config.num_channels     ,   config.embedding_size     ,   kernel_size=3     ,   stride=2     ,   activation=config.hidden_act       )
         snake_case											=							config.num_channels




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							pixel_values.shape[1]
         if num_channels != self.num_channels:
              raise ValueError(
                  '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''       )
         snake_case											=							self.embedder(__snake_case       )
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case = 2       ):
         super().__init__()
         snake_case											=							nn.Convad(__snake_case     ,   __snake_case     ,   kernel_size=1     ,   stride=__snake_case     ,   bias=__snake_case       )
         snake_case											=							nn.BatchNormad(__snake_case       )




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.convolution(__snake_case       )
         snake_case											=							self.normalization(__snake_case       )
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case       ):
         super().__init__()

         snake_case											=							nn.AdaptiveAvgPoolad((1, 1)       )
         snake_case											=							nn.Sequential(
             nn.Convad(__snake_case     ,   __snake_case     ,   kernel_size=1       )     ,   nn.ReLU()     ,   nn.Convad(__snake_case     ,   __snake_case     ,   kernel_size=1       )     ,   nn.Sigmoid()     ,   )




    def 						a_     (			self     ,   __snake_case       ):
         # b c h w -> b c 1 1
         snake_case											=							self.pooler(__snake_case       )
         snake_case											=							self.attention(__snake_case       )
         snake_case											=							hidden_state * attention
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case = 1       ):
         super().__init__()
         snake_case											=							in_channels != out_channels or stride != 1
         snake_case											=							max(1     ,   out_channels // config.groups_width       )
         snake_case											=							(
             RegNetShortCut(__snake_case     ,   __snake_case     ,   stride=__snake_case       ) if should_apply_shortcut else nn.Identity()
         )
         snake_case											=							nn.Sequential(
             RegNetConvLayer(__snake_case     ,   __snake_case     ,   kernel_size=1     ,   activation=config.hidden_act       )     ,   RegNetConvLayer(__snake_case     ,   __snake_case     ,   stride=__snake_case     ,   groups=__snake_case     ,   activation=config.hidden_act       )     ,   RegNetConvLayer(__snake_case     ,   __snake_case     ,   kernel_size=1     ,   activation=__snake_case       )     ,   )
         snake_case											=							ACTaFN[config.hidden_act]




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							hidden_state
         snake_case											=							self.layer(__snake_case       )
         snake_case											=							self.shortcut(__snake_case       )
         hidden_state += residual
         snake_case											=							self.activation(__snake_case       )
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case = 1       ):
         super().__init__()
         snake_case											=							in_channels != out_channels or stride != 1
         snake_case											=							max(1     ,   out_channels // config.groups_width       )
         snake_case											=							(
             RegNetShortCut(__snake_case     ,   __snake_case     ,   stride=__snake_case       ) if should_apply_shortcut else nn.Identity()
         )
         snake_case											=							nn.Sequential(
             RegNetConvLayer(__snake_case     ,   __snake_case     ,   kernel_size=1     ,   activation=config.hidden_act       )     ,   RegNetConvLayer(__snake_case     ,   __snake_case     ,   stride=__snake_case     ,   groups=__snake_case     ,   activation=config.hidden_act       )     ,   RegNetSELayer(__snake_case     ,   reduced_channels=int(round(in_channels / 4       )       )       )     ,   RegNetConvLayer(__snake_case     ,   __snake_case     ,   kernel_size=1     ,   activation=__snake_case       )     ,   )
         snake_case											=							ACTaFN[config.hidden_act]




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							hidden_state
         snake_case											=							self.layer(__snake_case       )
         snake_case											=							self.shortcut(__snake_case       )
         hidden_state += residual
         snake_case											=							self.activation(__snake_case       )
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case = 2     ,   __snake_case = 2     ,   ):
         super().__init__()

         snake_case											=							RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

         snake_case											=							nn.Sequential(
             # downsampling is done in the first layer with stride of 2
             layer(
                 __snake_case     ,   __snake_case     ,   __snake_case     ,   stride=__snake_case     ,   )     ,   *[layer(__snake_case     ,   __snake_case     ,   __snake_case       ) for _ in range(depth - 1       )]     ,   )




    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							self.layers(__snake_case       )
         return hidden_state






class     A__   (       nn.Module      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         super().__init__()
         snake_case											=							nn.ModuleList([]       )
         # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
         self.stages.append(
             RegNetStage(
                 __snake_case     ,   config.embedding_size     ,   config.hidden_sizes[0]     ,   stride=2 if config.downsample_in_first_stage else 1     ,   depth=config.depths[0]     ,   )       )
         snake_case											=							zip(config.hidden_sizes     ,   config.hidden_sizes[1:]       )
         for (in_channels, out_channels), depth in zip(__snake_case     ,   config.depths[1:]       ):
              self.stages.append(RegNetStage(__snake_case     ,   __snake_case     ,   __snake_case     ,   depth=__snake_case       )       )




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = False     ,   __snake_case = True       ):
         snake_case											=							() if output_hidden_states else None

         for stage_module in self.stages:
              if output_hidden_states:
                   snake_case											=							hidden_states + (hidden_state,)

              snake_case											=							stage_module(__snake_case       )

         if output_hidden_states:
              snake_case											=							hidden_states + (hidden_state,)

         if not return_dict:
              return tuple(v for v in [hidden_state, hidden_states] if v is not None       )

         return BaseModelOutputWithNoAttention(last_hidden_state=__snake_case     ,   hidden_states=__snake_case       )






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						RegNetConfig
    __magic_name__         =						'regnet'
    __magic_name__         =						'pixel_values'
    __magic_name__         =						True
    def 						a_     (			self     ,   __snake_case       ):
         if isinstance(__snake_case     ,   nn.Convad       ):
              nn.init.kaiming_normal_(module.weight     ,   mode='''fan_out'''     ,   nonlinearity='''relu'''       )
         elif isinstance(__snake_case     ,   (nn.BatchNormad, nn.GroupNorm)       ):
              nn.init.constant_(module.weight     ,   1       )
              nn.init.constant_(module.bias     ,   0       )




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         if isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							value






_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

_SCREAMING_SNAKE_CASE     :					Optional[int]      =       R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"

@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'			,   snake_case__			,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class     A__   (       snake_case__      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         super().__init__(__snake_case       )
         snake_case											=							config
         snake_case											=							RegNetEmbeddings(__snake_case       )
         snake_case											=							RegNetEncoder(__snake_case       )
         snake_case											=							nn.AdaptiveAvgPoolad((1, 1)       )
         # Initialize weights and apply final processing
         self.post_init()




    @add_start_docstrings_to_model_forward(__snake_case       )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC     ,   output_type=__snake_case     ,   config_class=_CONFIG_FOR_DOC     ,   modality='''vision'''     ,   expected_output=_EXPECTED_OUTPUT_SHAPE     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = None       ):
         snake_case											=							(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         snake_case											=							return_dict if return_dict is not None else self.config.use_return_dict

         snake_case											=							self.embedder(__snake_case       )

         snake_case											=							self.encoder(
             __snake_case     ,   output_hidden_states=__snake_case     ,   return_dict=__snake_case       )

         snake_case											=							encoder_outputs[0]

         snake_case											=							self.pooler(__snake_case       )

         if not return_dict:
              return (last_hidden_state, pooled_output) + encoder_outputs[1:]

         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=__snake_case     ,   pooler_output=__snake_case     ,   hidden_states=encoder_outputs.hidden_states     ,   )






@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '			,   snake_case__			,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class     A__   (       snake_case__      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         super().__init__(__snake_case       )
         snake_case											=							config.num_labels
         snake_case											=							RegNetModel(__snake_case       )
         # classification head
         snake_case											=							nn.Sequential(
             nn.Flatten()     ,   nn.Linear(config.hidden_sizes[-1]     ,   config.num_labels       ) if config.num_labels > 0 else nn.Identity()     ,   )
         # initialize weights and apply final processing
         self.post_init()




    @add_start_docstrings_to_model_forward(__snake_case       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT     ,   output_type=__snake_case     ,   config_class=_CONFIG_FOR_DOC     ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,   )
    def 						a_     (			self     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   __snake_case = None     ,   ):
         snake_case											=							return_dict if return_dict is not None else self.config.use_return_dict

         snake_case											=							self.regnet(__snake_case     ,   output_hidden_states=__snake_case     ,   return_dict=__snake_case       )

         snake_case											=							outputs.pooler_output if return_dict else outputs[1]

         snake_case											=							self.classifier(__snake_case       )

         snake_case											=							None

         if labels is not None:
              if self.config.problem_type is None:
                   if self.num_labels == 1:
                        snake_case											=							'''regression'''
                   elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                        snake_case											=							'''single_label_classification'''
                   else:
                        snake_case											=							'''multi_label_classification'''
              if self.config.problem_type == "regression":
                   snake_case											=							MSELoss()
                   if self.num_labels == 1:
                        snake_case											=							loss_fct(logits.squeeze()     ,   labels.squeeze()       )
                   else:
                        snake_case											=							loss_fct(__snake_case     ,   __snake_case       )
              elif self.config.problem_type == "single_label_classification":
                   snake_case											=							CrossEntropyLoss()
                   snake_case											=							loss_fct(logits.view(-1     ,   self.num_labels       )     ,   labels.view(-1       )       )
              elif self.config.problem_type == "multi_label_classification":
                   snake_case											=							BCEWithLogitsLoss()
                   snake_case											=							loss_fct(__snake_case     ,   __snake_case       )

         if not return_dict:
              snake_case											=							(logits,) + outputs[2:]
              return (loss,) + output if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=__snake_case     ,   logits=__snake_case     ,   hidden_states=outputs.hidden_states       )








import os
from distutils.util import strtobool
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     for e in env_keys:
          snake_case											=							int(os.environ.get(UpperCamelCase_				,-1							)							)
          if val >= 0:
               return val
     return default
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_=False							):



     """simple docstring"""






     snake_case											=							os.environ.get(UpperCamelCase_				,str(UpperCamelCase_							)							)
     return strtobool(UpperCamelCase_							) == 1  # As its name indicates `strtobool` actually returns an int...

def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_="no"							):



     """simple docstring"""






     snake_case											=							os.environ.get(UpperCamelCase_				,str(UpperCamelCase_							)							)
     return value




